Secure communication system based on quantum reserve pool network

Abstract

The present invention discloses a secure communication system based on a quantum reserve pool network, in the field of secure communication technology. The system includes a transmitting end module, a receiving end module, and a system monitoring and management module. The transmitting end module includes a signal source preprocessing unit and a chaotic encryption unit. The present invention realizes efficient encryption and decryption of the secure communication system by introducing a combination of a Lorenz chaotic system and a quantum reserve pool network. At the transmitting end, the Lorenz chaotic system is used to encrypt the transmitted information, and the chaotic carrier is superimposed on the transmission sequence through chaotic masking, thereby improving the confidentiality of the information. At the same time, through the training and optimization of the core unit of the quantum reserve pool network, the chaotic carrier can be accurately predicted, thereby realizing accurate decryption of the encrypted signal. This method of combining chaos theory with quantum computing not only enhances the security of the communication system, but also improves the efficiency and stability of information transmission.

Description

Technical Field

[0001] The present invention relates to the field of secure communication technology, specifically to a secure communication system based on a quantum reserve pool network. Background Art

[0002] With the rapid development of modern communication technology, confidential communication, as a core link in the field of information security, has become increasingly important. With the increasing demand for information security in the financial, military, and medical fields, the development of an efficient and reliable confidential communication system has become a top priority. Traditional encryption methods, such as symmetric encryption and asymmetric encryption, can ensure communication security to a certain extent. However, in the face of increasingly complex attack methods and ever-increasing computing power, their security is facing severe challenges. Therefore, exploring new encryption technologies to cope with the ever-escalating information security threats has become a hot topic in current research.

[0003] Traditional secure communication technologies rely primarily on complex algorithms and key management strategies. However, these methods have numerous shortcomings in practical applications. On the one hand, the computational complexity of traditional encryption algorithms is high, resulting in decreased communication efficiency. On the other hand, the key management and distribution process can easily become a security vulnerability. Once cracked, the security of the entire communication system will be seriously threatened. Furthermore, traditional secure communication technologies often struggle to cope with new attack methods, raising questions about their long-term security.

[0004] Therefore, the development of a confidential communication system based on a quantum reserve pool network is widely used in sensitive areas such as financial transactions, military communications, and medical data transmission, providing strong protection for information security. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a secure communication system based on a quantum reserve pool network. The system integrates the advanced technologies of chaotic encryption and quantum computing to achieve efficient and confidential information transmission. By encrypting information through the Lorenz chaotic system and combining it with the core decryption technology of the quantum reserve pool network, it not only improves the security of information transmission but also ensures the integrity and accuracy of the data.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a secure communication system based on a quantum reserve pool network, the system comprising a transmitting end module, a receiving end module and a system monitoring and management module;

[0007] The transmitting end module includes a signal source preprocessing unit and a chaotic encryption unit; the signal source preprocessing unit generates an original signal sequence according to different application scenarios, performs format conversion and encoding preprocessing operations, and records key signal features and parameter information; the chaotic encryption unit includes a chaotic system configuration subunit and an encryption execution subunit; the chaotic system configuration subunit selects the Lorenz chaotic system to convert the transmitted information into a chaotic carrier signal, and encrypts the system parameters with the help of a cryptographic algorithm; the encryption execution subunit generates a chaotic carrier signal and superimposes it with the original signal to form an encrypted signal, and strictly monitors the signal indicators throughout the encryption process. Once an abnormality occurs, it will be quickly processed and warned, and the encrypted signal will be sent to the receiving end via the channel synchronously;

[0008] The receiving end module includes a quantum reserve pool network core unit and a signal decryption and post-processing unit; the quantum reserve pool network core unit includes a variational quantum circuit VQC integration subunit and a training and optimization subunit, wherein the variational quantum circuit VQC integration subunit uses VQC to build a QRC network; wherein the training and optimization subunit receives the encrypted signal and chaotic carrier, groups them, uses the Bayesian optimization algorithm to build a model based on the Gaussian process, and iteratively selects evaluation points to globally optimize the network parameters; the signal decryption and post-processing unit uses the trained network to predict the chaotic carrier, subtracts the obtained target signal from the encrypted signal to complete decryption, checks the integrity, activates the error correction mechanism if there is a problem, and restores the signal to its original format to transmit it to the subsequent application system or user terminal;

[0009] The system monitoring and management module includes a performance monitoring and evaluation submodule and a parameter adjustment and optimization submodule. The performance monitoring and evaluation submodule monitors the signal strength and signal-to-noise ratio performance indicators of the transmitter and receiver in real time during system operation, regularly and comprehensively evaluates and analyzes changing trends and potential problems, and generates reports to provide data for optimization. The parameter adjustment and optimization submodule dynamically adjusts the parameters of the chaotic system, quantum reserve pool network, and communication channel based on the monitoring and evaluation results.

[0010] Furthermore, the Lorenz chaotic system in the transmitting module encrypts the transmitted information, and its equation is: , , , ,in is the state variable of the system, 、 、 are the parameters of the system, 、 、 Respectively The time derivative, is the output variable, which is time function, is a constant, is the input variable, which is time Function, in the process of setting parameters, uses the Advanced Encryption Standard algorithm AES to generate a strong key , encrypt the parameters, and set the encrypted parameters to ,but , , , and at the same time, for the initial value , also encrypted with the same key.

[0011] Furthermore, the transmitting module uses a chaotic masking method to superimpose the chaotic carrier with a certain masking coefficient on the transmission sequence. The masking coefficient α is expressed as: ,in, represents the peak-to-peak value of the information M(t), Represents the peak-to-peak value of the chaotic carrier X(t).

[0012] Furthermore, the encrypted signal in the sending module The generation formula is: ,in represents the original signal sequence, Represents the chaotic carrier signal. During the encryption process, the amplitude of the signal is monitored in real time. , the formula is: ,in For three-dimensional signals, set the amplitude threshold range , or , adjust the chaotic system parameters or reprocess the original signal, regenerate the chaotic carrier and perform superposition encryption.

[0013] Furthermore, the QRC network in the receiving module is composed of a data encoding layer and a layer-variant layer. The data encoding layer is composed of an H gate. The initial state is transformed by the H gate. For the input quantum bit state , when passing through the H gate, it is converted into a superposition state according to the transformation rule of the H gate, the formula is: The variation layer consists of multiple CNOT gates and R gates, where the CNOT gate operates on adjacent qubits according to its control rules. and , control bit for , then after the CNOT gate acts The state is flipped, and its matrix is ​​represented as , realizing information transmission and entanglement between quantum bits, where the Ry gate controls the rotation angle The quantum bit is rotated around the y-axis, and its matrix is ​​expressed as: .

[0014] Furthermore, in the receiving end module, the specific steps of constructing a model based on the Gaussian process using the Bayesian optimization algorithm and iteratively selecting evaluation points to perform global optimization of network parameters are as follows:

[0015] (1) Data packet: receiving encrypted signals and chaotic carrier , perform reasonable data grouping, suppose N groups of signals are received, N is the total number of groups, The group is used as the training set, the middle group as a test set, and finally group as the validation set, and ;

[0016] (2) Constructing Gaussian process model:

[0017] Define input space and output space: Input space ,in It is an encrypted signal and chaotic carrier The combination of values ​​at different time points or with different characteristics, , Represents the output space corresponding to the i-th time point , Is with input Related target values;

[0018] Determine the Gaussian process representation: The Gaussian process representation is: ,in is the mean function, is the covariance function, and the formula is: , is the signal variance, is the length scale parameter, Indicates the distance between two points;

[0019] (3) Predicting the output of a new input point based on Bayesian inference: Given the training set data , for the new input point , predict its output , the prediction result is calculated according to the formula, the formula is: ,in is the mean, is the variance, and the calculation formula is: , , is the covariance matrix of the observed data points, with elements , , is the variance of the observation noise, is the identity matrix, Is a new input point The covariance vector with all points in the training set, whose elements , , is the covariance of the new input point itself;

[0020] (4) Iteratively select evaluation points for global optimization of network parameters: Initialize network parameters, select evaluation points from the parameter space based on the acquisition function, apply their parameter settings to the network, use the training set data to predict and calculate the output through the Gaussian process model, and update the model based on the difference with the actual target value. Repeat until the maximum number of iterations is reached;

[0021] (5) Verification and application: Use the validation set data to verify the optimized network, calculate the performance indicators to evaluate the effect, apply the optimized network to actual scenarios, and process new data.

[0022] Furthermore, the decryption of the target signal in the receiving module uses the trained quantum reserve pool network to predict the chaotic carrier , the received encrypted signal Input into the quantum reserve pool network, after the network's forward propagation calculation, the output function of the quantum reserve pool network is set to be ,in Chaotic carrier predicted by trained network parameters , from the received encrypted signal Subtract Get the target signal Complete decryption, the formula is: .

[0023] Furthermore, the chaotic system equation after decryption at the receiving end in the receiving end module is as follows: , the expression equation is: , , , ,in is the output variable, which is time function, is the input variable, which is time function, are the parameters of the system.

[0024] Furthermore, in the receiving module, the integrity check is performed, assuming that the original data is a binary sequence with a length of bit, generating polynomial , the formula is: , where x is the variable of the polynomial, r represents the degree of the polynomial, It is a binary coefficient with a value of 0 or 1. Add at the end of 0, and get a new data sequence ,at this time The length is Position, will Treated as a polynomial , whose coefficient is The binary bits in Divide by the generator polynomial , perform binary division without considering carry and borrow, and the remainder is It's the checksum , it is a The binary sequence of bits will be calculated to get the checksum Verify with pre-set standards and For comparison, ,illustrate No errors occurred during transmission or storage, the data is complete, , the data is incorrect or incomplete.

[0025] Compared with existing technologies, this secure communication system based on quantum reserve pool network has the following beneficial effects:

[0026] First, the present invention combines the Lorenz chaotic system with a quantum reserve pool network to achieve efficient encryption and decryption of secure communication systems. At the transmitting end, the Lorenz chaotic system is used to encrypt the transmitted information, and the chaotic carrier is superimposed on the transmission sequence through chaotic masking, thereby improving the confidentiality of the information. At the same time, through the training and optimization of the core units of the quantum reserve pool network, the chaotic carrier can be accurately predicted, thereby achieving accurate decryption of the encrypted signal. This method of combining chaos theory with quantum computing not only enhances the security of the communication system, but also improves the efficiency and stability of information transmission.

[0027] Second, the present invention applies the Bayesian optimization algorithm in the receiving module to construct a model based on the Gaussian process, and performs global optimization of network parameters. Through reasonable data grouping, building a Gaussian process model, predicting the output of new input points based on Bayesian inference, and iteratively screening evaluation points, it is possible to achieve precise adjustment of network parameters, thereby improving the prediction accuracy and decryption effect of the quantum reserve pool network, which not only improves the performance of the communication system, but also provides more reliable technical support for subsequent application scenarios.

[0028] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0030] Figure 1 Flowchart of a secure communication system based on a quantum reserve pool network;

[0031] Figure 2 Flowchart for confidential communications;

[0032] Figure 3 Flowchart of a single-channel chaotic secure communication scheme based on drive-response synchronization. DETAILED DESCRIPTION

[0033] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0034] Example 1

[0035] Military Command and Communication Scenario Application Example

[0036] At the sending end, the signal source preprocessing unit generates the original signal sequence based on the military combat plan and troop deployment information. For example, it generates a signal sequence containing the coordinates of each troop's marching route, combat mission details, and weapon and equipment parameter information, and converts it into a specific format suitable for encryption processing. At the same time, it records the key characteristics of the signal (such as data type, urgency level identification) and parameter information (such as signal frequency range, data volume).

[0037] In the chaotic encryption unit, the chaotic system configuration subunit selects the Lorenz chaotic system, whose equation is: , , , , using the Advanced Encryption Standard algorithm AES to generate strong keys , encrypt the parameters, and set the encrypted parameters to ,but , , , and at the same time, for the initial value , also uses the same key to encrypt, transform the transmission information into a chaotic carrier signal, the encryption execution sub-unit adopts a chaotic masking method, and sets the information The peak-to-peak value is , chaotic carrier The peak-to-peak value is , calculate the masking coefficient according to the formula: , superimpose the chaotic carrier and the original signal to form an encrypted signal The generation formula is: , during the encryption process, real-time monitoring of signal amplitude , the formula is , set the amplitude threshold range to ,like or , then adjust the chaotic system parameters or reprocess the original signal, regenerate the chaotic carrier and superimpose encryption, and finally send the encrypted signal to the receiver through a secure channel.

[0038] Receiver operation: The variational quantum circuit VQC integrated subunit of the core unit of the quantum reserve pool network uses VQC to build a QRC network, where the QRC network consists of data encoding and layer variation. The data encoding layer consists of H gates. The initial state is transformed through the H gate. For the input quantum bit state , when passing through the H gate, it is converted into a superposition state according to the transformation rule of the H gate, the formula is: The variation layer consists of multiple CNOT gates and R gates. The CNOT gate operates on adjacent qubits according to its control rules. and , control bit for , then after the CNOT gate acts The state is flipped, and its matrix is ​​represented as , realizing information transmission and entanglement between quantum bits, where the Ry gate controls the rotation angle The quantum bit is rotated around the y-axis, and its matrix is ​​expressed as: After receiving the encrypted signal and chaotic carrier, the training and optimization subunit groups the received N groups of signals. The group is used as the training set, the middle group as a test set, and finally group as the validation set, and , build a Gaussian process model, define the input space and output space, input space ,in , output space , determine the Gaussian process expression as: , where the mean function and covariance function According to the given formula, the covariance function is: , is the signal variance, is the length scale parameter, Represents the distance between two points. Based on the Bayesian optimization algorithm and the Gaussian process, a model is constructed to predict the output of the new input point. The formula is: , its mean and variance The calculation formula is: , , the network parameters are globally optimized by iteratively screening evaluation points, the network parameters are initialized, the output is predicted and calculated using the Gaussian process model using the training set data, and the loss function value is calculated based on the difference with the actual target value to update the model, and repeated until the maximum number of iterations is reached.

[0039] The signal decryption and post-processing unit uses the trained network to predict the chaotic carrier , the received encrypted signal Input into the quantum reserve pool network, after the network's forward propagation calculation, the output function of the quantum reserve pool network is set to be , the target signal is obtained by subtracting the predicted chaotic carrier from the received encrypted signal: , let the state variables of the system be , the decrypted chaotic system equation is: , , , , perform integrity check on the decrypted signal, assuming that the original data is a binary sequence with a length of bit, generating polynomial , add at the end of the original data 0 to get a new data sequence ,Will Treated as a polynomial Divide by , perform binary division without considering carry and borrow, and the remainder is It's the checksum , it is a The binary sequence of bits will calculate the checksum Verify with pre-set standards and For comparison, ,illustrate No errors occurred during transmission or storage, the data is complete, , it indicates that the data is wrong or incomplete. If the data is complete, the signal will be restored to its original format and transmitted to the combat command system of the military command center for the military commander to make combat decisions.

[0040] The system monitoring and management module operates, and the performance monitoring and evaluation submodule monitors the signal strength and signal-to-noise ratio performance indicators of the transmitter and receiver in real time during military communications. For example, data is collected every 5 seconds. If the signal strength in a certain area suddenly drops by 30% or the signal-to-noise ratio falls below the set threshold (such as 15dB), the time, location and related data will be recorded in a timely manner, and the cause will be analyzed. A comprehensive evaluation will be conducted regularly (hourly) and a report will be generated. The report content includes signal quality trend analysis and potential interference source speculation, providing data support for subsequent optimization. The parameter adjustment and optimization submodule dynamically adjusts the parameters of the chaotic system, quantum reserve pool network and communication channel based on the monitoring and evaluation results. If the signal strength drop is found to be due to channel attenuation, the transmission power of the communication channel can be appropriately increased. If the decryption accuracy of the quantum reserve pool network drops, the network training parameters will be readjusted, such as increasing the number of training times or adjusting the acquisition function parameters in the Bayesian optimization algorithm. If the chaotic encryption effect is affected, the AES key will be regenerated and the chaotic system parameter encryption settings will be updated.

[0041] In summary: In military command communications, this secure communication system uses chaotic encryption and strict signal processing at the sending end, and utilizes the Lorenz chaotic system equation and related encryption methods to ensure the secure conversion and transmission of information. The receiving end relies on the construction, optimization and predictive decryption functions of the quantum reserve pool network, combined with integrity verification to ensure data accuracy. The system monitoring and management module continuously monitors, evaluates and dynamically adjusts parameters, effectively improving the confidentiality and reliability of military information transmission as a whole, and meeting the communication needs of military command operations.

[0042] Example 2:

[0043] At the sending end, the signal source preprocessing unit generates an original signal sequence for financial transaction data, such as customer account fund change information, securities trading instructions, and credit approval results, and performs format conversion and encoding preprocessing operations, recording the key characteristics of the signal (such as transaction amount accuracy, transaction timestamp format) and parameters (such as data transmission rate requirements and data encryption level).

[0044] The chaotic system configuration subunit of the chaotic encryption unit selects the Lorenz chaotic system, and its equation is: , , , , using a key generated using AES Encryption parameters 、 、 and initial value, let the encrypted parameter be , , and at the same time, for the initial value 、 、 , also uses the same key for encryption, after converting the transmission information into a chaotic carrier signal, let the peak-to-peak value of the transaction data be , the peak-to-peak value of the chaotic carrier is , calculate the masking coefficient according to the formula: , the generated chaotic carrier signal is superimposed with the original signal to form an encrypted signal, the formula is: ,in represents the original signal sequence, Represents the chaotic carrier signal. During the encryption process, the amplitude of the signal is monitored in real time. , the formula is: ,in For three-dimensional signals, set the amplitude threshold range , or , adjust the chaotic system parameters or reprocess the original signal, regenerate the chaotic carrier and perform superposition encryption to ensure that the signal meets the requirements and is sent to the receiving end through the channel.

[0045] At the receiving end, the variational quantum circuit VQC integrated subunit of the quantum reserve pool network core unit builds a QRC network. The training and optimization subunit receives the encrypted signal and chaotic carrier and groups them. Assume that the received signal is Group, of which The group is the training set, The group is the test set, group is the validation set, and , build a Gaussian process model, determine the input and output space, input space ,in , output space , the Gaussian process is expressed as: , where the mean function and covariance function According to the given formula, the covariance function is: is the signal variance, is the length scale parameter, Represents the distance between two points. Based on the Bayesian optimization algorithm and the Gaussian process, a model is constructed to predict the output of the new input point. The formula is: , its mean and variance The calculation formula is: , , the network parameters are globally optimized by iteratively screening evaluation points, the network parameters are initialized, the output is predicted and calculated using the Gaussian process model using the training set data, and the loss function value is calculated based on the difference with the actual target value to update the model, and repeated until the maximum number of iterations is reached.

[0046] The signal decryption and post-processing unit uses the trained network to predict the chaotic carrier , the received encrypted signal Input network calculation , subtract the chaotic carrier from the encrypted signal to get the decrypted target signal , the decrypted chaotic system equation expression is: , , , , perform integrity check, set the original transaction data is a binary sequence with a length of bit, generating polynomial , add at the end of the original data 0 get ,Will Treated as a polynomial Divide by , perform binary division without considering carry and borrow, and the remainder is It's the checksum , it is a The binary sequence of bits will be calculated to get the checksum Verify with pre-set standards and After comparison, if the data is complete, the signal will be restored to its original format and transmitted to the financial institution's data processing center server for subsequent transaction records, account updates, and risk assessment operations.

[0047] The system monitoring and management module operates, and the performance monitoring and evaluation submodule monitors the signal strength and signal-to-noise ratio performance indicators during the financial data transmission process in real time. For example, each transaction data is monitored. If the signal strength fluctuation exceeds 20% or the signal-to-noise ratio is lower than 20dB, an early warning is immediately issued and relevant information is recorded. A comprehensive evaluation of the entire day's transmission data is conducted every day, analyzing the relationship between signal quality and transaction time and transaction type factors, and generating a report. The parameter adjustment and optimization submodule dynamically adjusts the system parameters based on the monitoring and evaluation results. If it is found that the large amount of transaction data in a certain period causes signal congestion and a decrease in the signal-to-noise ratio, the communication channel bandwidth can be temporarily increased or the processing speed of the quantum reserve pool network can be optimized. If problems are found in the encryption and decryption of certain complex transaction data (such as cross-border financial derivatives transaction data), the chaotic system parameters can be adjusted or the training strategy of the quantum reserve pool network can be improved to ensure the secure and accurate transmission of financial data and maintain the stable operation of financial transactions.

[0048] To summarize: For data transmission within financial institutions, the system performs chaotic encryption operations based on the characteristics of financial data at the sending end, uses a specific formula to calculate the masking coefficient, and the quantum reserve pool network at the receiving end completes signal processing, decryption, and integrity verification. The system monitoring and management module controls signal performance in real time and adjusts parameters as needed, effectively ensuring the security and stability of financial transaction data during transmission and meeting the confidential communication requirements of financial services.

[0049] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A secure communication system based on a quantum reserve pool network, characterized by: The system includes a sending end module, a receiving end module and a system monitoring and management module; The transmitting end module includes a signal source preprocessing unit and a chaotic encryption unit; the signal source preprocessing unit generates an original signal sequence according to different application scenarios, performs format conversion and encoding preprocessing operations, and records key signal features and parameter information; the chaotic encryption unit includes a chaotic system configuration subunit and an encryption execution subunit; the chaotic system configuration subunit selects the Lorenz chaotic system to convert the transmitted information into a chaotic carrier signal, and encrypts the system parameters with the help of a cryptographic algorithm; the encryption execution subunit superimposes the chaotic carrier signal and the original signal into an encrypted signal, monitors the signal indicators throughout the encryption process, and quickly handles and issues warnings in the event of an abnormality; After synchronization, the encrypted signal is sent to the receiving end through the channel. In the encryption execution subunit, the chaotic carrier signal is superimposed on the transmission sequence with a certain masking coefficient using a chaotic masking method. The masking coefficient α is expressed as: Among them, M P-P represents the peak-to-peak value of the information M(t), X P-P Represents the peak-to-peak value of the chaotic carrier signal X(t); The receiving end module includes a quantum reserve pool network core unit and a signal decryption and post-processing unit; the quantum reserve pool network core unit includes a variational quantum circuit VQC integration subunit and a training and optimization subunit, wherein the variational quantum circuit VQC integration subunit uses VQC to build a QRC network; the training and optimization subunit receives the encrypted signal and the chaotic carrier signal, groups them, uses the Bayesian optimization algorithm to build a model based on the Gaussian process, and iteratively selects evaluation points to globally optimize the network parameters; the signal decryption and post-processing unit uses the trained network to predict the chaotic carrier signal, subtracts the obtained chaotic carrier signal from the encrypted signal to complete decryption, checks the integrity, activates the error correction mechanism if there is a problem, and restores the signal to its original format to transmit it to the subsequent application system or user terminal; The system monitoring and management module includes a performance monitoring and evaluation submodule and a parameter adjustment and optimization submodule. The performance monitoring and evaluation submodule monitors the signal strength and signal-to-noise ratio performance indicators of the transmitter and receiver in real time during system operation, regularly and comprehensively evaluates and analyzes changing trends and potential problems, and generates reports to provide data for optimization. The parameter adjustment and optimization submodule dynamically adjusts the parameters of the chaotic system, quantum reserve pool network, and communication channel based on the monitoring and evaluation results.

2. The secure communication system based on the quantum reserve pool network according to claim 1, characterized in that: The Lorenz chaotic system in the transmitter module encrypts the transmitted information, and its equation is: r(t)=ku(t)+x1, where x1, x2, x3 are the state variables of the system, σ, ρ, b are the parameters of the system, They represent the derivatives of x1, x2, and x3 with respect to time, r(t) is the output variable, which is a function of time t, k is a constant, and u(t) is the input variable, which is a function of time t. In the process of setting parameters, the Advanced Encryption Standard algorithm AES is used to generate a strong key K to encrypt the parameters. The encrypted parameters are E σ 、E ρ 、E b , then E σ =AES K (σ), E ρ =AES K (ρ), E b =AES K (b) At the same time, the initial values ​​x0, y0, and z0 are also encrypted using the same key.

3. The secure communication system based on the quantum reserve pool network according to claim 1, characterized in that: The encryption signal u(t) in the transmitting module is generated by the formula: u(t) = X(t) + M(t), where M(t) represents the original signal sequence and X(t) represents the chaotic carrier signal. During the encryption process, the amplitude A(t) of the signal is monitored in real time, and the formula is: Where u(t) is a three-dimensional signal, and the amplitude threshold range is [A min , A max ], if A(t) min Or A(t)>A max , adjust the chaotic system parameters or reprocess the original signal, regenerate the chaotic carrier signal and perform superposition encryption.​ 4. The secure communication system based on the quantum reserve pool network according to claim 1, characterized in that: The QRC network in the receiving end module consists of a data coding layer and a variation layer. The data coding layer consists of an H gate. The initial state is transformed through the H gate. For the input quantum bit state |ψ>, when it passes through the H gate, it is converted into a superposition state according to the transformation rule of the H gate. The formula is: The variation layer consists of multiple CNOT gates and R gates. The CNOT gate operates on adjacent quantum bits according to its control rules. For two quantum bits |q1> and |q2>, the control bit |q1> is |1>, then the state of |q2> is flipped after the CNOT gate is applied. Its matrix is ​​expressed as Realize information transmission and entanglement between quantum bits; the R gate rotates the quantum bit around the y-axis by controlling the rotation angle θ, and its matrix is ​​expressed as:

5. The secure communication system based on the quantum reserve pool network according to claim 1, characterized in that: The specific steps of using the Bayesian optimization algorithm in the receiving end module to build a model based on the Gaussian process and iteratively select evaluation points to perform global optimization of network parameters are as follows: (1) Data grouping: Receive the encrypted signal u(t) and the chaotic carrier signal X(t), and group them into data groups. N is the total number of groups. The first N1 groups are used as training sets, the middle N2 groups are used as test sets, and the last N3 groups are used as validation sets, and N = N1 + N2 + N3. (2) Constructing Gaussian process model: Define input space and output space: Input space where x i It is the combination of the encrypted signal u(t) and the chaotic carrier signal X(t) at different time points or with different characteristics, x i =[u(t i ),X(t i )],t i Indicates the i-th time point, the corresponding output space y i is the same as the input x i Related target values; The Gaussian process is expressed as: f(x) ~ GP(m(x), k(x, x′)), where m(x) is the mean function and k(x, x′) is the covariance function. The formula is: is the signal variance, l is the length scale parameter, and ||xx′|| represents the distance between two points; (3) Predict the output of a new input point based on Bayesian inference: Given the training set data (X, y), for a new input point x * , predict its output y * , the prediction result is calculated according to the formula, the formula is: where μ * is the mean, is the variance, and the calculation formula is: K is the covariance matrix of the observed data points, and the element K ij =k(x i , x j ),i,j=1,2,…,N1, is the variance of the observation noise, I is the identity matrix, k * is the new input point x * The covariance vector with all points in the training set, whose elements k *i =k(x * ,x i ), i = 1, 2, ..., k(x * , x * ) is the covariance of the new input point itself; (4) Iteratively select evaluation points for global optimization of network parameters: Initialize network parameters, select evaluation points from the parameter space based on the acquisition function, apply their parameters to the network, use the training set data to predict and calculate the output through the Gaussian process model, and update the model based on the difference with the actual target value. Repeat until the maximum number of iterations is reached; (5) Verification and application: Use the validation set data to verify the optimized network, calculate the performance indicators to evaluate the effect, apply the optimized network to actual scenarios, and process new data.

6. The secure communication system based on the quantum reserve pool network according to claim 1, characterized in that: The target signal in the receiving module is decrypted by using the trained quantum reserve network to predict the chaotic carrier signal X′(t). The received encrypted signal u(t) is input into the quantum reserve network. After the forward propagation calculation of the network, the output function of the quantum reserve network is f QRC (u(t), θ), where θ is the chaotic carrier signal predicted by the trained network parameters X′(t) = f QRC (u(t), θ), subtract X′(t) from the received encrypted signal u(t) to obtain the target signal M′(t) to complete decryption, the formula is: M′(t) = u(t) - X′(t).

7. The secure communication system based on the quantum reserve pool network according to claim 1, characterized in that: The chaotic system equation after decryption in the receiving end module is: r(t)=ku(t)+y1, where y1, y2, y3 are the state variables of the system, r(t) is the output variable, which is a function of time t, u(t) is the input variable, which is a function of time t, and σ, ρ, b, k are the parameters of the system.

8. The secure communication system based on the quantum reserve pool network according to claim 1, characterized in that: Integrity check is performed in the receiving module. The original data M(t) is a binary sequence with a length of n bits. The generating polynomial G(x) is as follows: G(x)=x r +g r-1 x r-1 +…+g1x+1, where x is the variable of the polynomial, r represents the degree of the polynomial, and g i , i=1,2,…,r-1,is a binary coefficient, which takes the value of 0 or 1. Add r zeros at the end of the original data M(t) to obtain a new data sequence M′(t). At this time, the length of M′(t) is n+r bits. Treat M′(t) as a polynomial M′(t), whose coefficients are the binary bits in M′(t). Divide M′(t) by the generating polynomial G(x) and perform binary division without considering carry and borrow. The remainder R(x) is the checksum C(M′(t)), which is an r-bit binary sequence. Compare the calculated checksum C(M′(t)) with the preset standard checksum C std For comparison, C(M′(t))=C std , indicating that M(t) has no errors during transmission or storage, the data is complete, and C(M′(t))≠C std , the data is incorrect or incomplete.

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